The exemplary embodiment relates to the field of information mining. It finds particular application in the automated mining of customer reviews for suggestions that the customer has made about the product or service which is the subject of the review.
Opinion mining is widely used in natural language processing, computational linguistics, and text mining by those seeking to obtain the opinions of customers and other reviewers on their products and services. Opinion mining generally aims at detecting whether the comments of reviewers are positive or negative. Opinions are often expressed on online review sites, social networks, personal blogs, e-forums, and in dedicated customer feedback pages of company websites.
In a customer review, for example, the reviewer may be asked to rate a particular product, on a scale of, for example, to 10, and to provide their comments on the product in a free text format, which allows the user to comment on the product in their own words. Sometimes, reviewers suggest improvements about the product that they are reviewing, which is quite different from expressing an opinion. Suggestions of improvements on a product are often made discursively, either by wishing the presence of a missing feature or component, or by regretting the absence of such a feature or component. This type of comment goes beyond the scope of traditional sentiment analysis, and thus the information provided by the suggestions is not collected. Suggestions could provide valuable information which could be analyzed in the context of business analytics, if techniques were available to extract them from the bulk of comments that are submitted.
To illustrate this problem, the following sentences give some instances of suggestions of improvements manually extracted from a corpus of user's comments about printers (the names of companies and products have been anonymized):                “ABC Co. should have made the bin deep enough to hold an entire ream (500 sheets).”        “Why ABC Co. didn't opt to throw in a 500-sheet tray with at least the standard 7 sizes (as per the 250-sheet tray) is beyond me.”        “This XYZ has filled the bill in those areas, however, for the price, BCD Co. should have considered throwing in more features and lowering the print costs.”        “I think they should have put a faster scanner on the machine, one at least as fast as the printer.”        “My only regret was that this unit could not fax!”        “If you're considering any CDE Co. inkjet, I think most people will be happy with the ZXY, which is only missing the automatic paper feed.”        
A manufacturer of printers, for example, would be interested in reviewing such suggestions. However, the process of extracting them manually from a corpus of reviews is very time consuming.
A system and method for mining text are provided which enable suggestions such as these to be automatically extracted from unstructured text by natural language processing.